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This paper describes a method for robust real time pattern matching. We first introduce a family of image distance measures, the "Image Hamming Distance Family". Members of this family are robust to occlusion, small geometrical transforms, light changes and non-rigid deformations. We then present a novel Bayesian framework for sequential hypothesis testing on finite populations. Based on this framework, we design an optimal rejection/acceptance sampling algorithm. This algorithm quickly determines whether two images are similar with respect to a member of the Image Hamming Distance Family. We also present a fast framework that designs a near-optimal sampling algorithm. Extensive experimental results show that the sequential sampling algorithm performance is excellent. Implemented on a Pentium 4 3GHz processor, detection of a pattern with 2197 pixels, in 640x480 pixel frames, where in each frame the pattern rotated and was highly occluded, proceeds at only 0.022 seconds per frame.
pattern matching, template matching, pattern detection, image similarity measures, Hamming distance, real time, sequential hypothesis testing, composite hypothesis, image statistics, Bayesian statistics, finite populations

O. Pele and M. Werman, "Robust Real-Time Pattern Matching Using Bayesian Sequential Hypothesis Testing," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 1427-1443, 2007.
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